Analysis of Access to Electricity and National Income
Iman
2025-01-07
Introduction
This report explores the relationship between access to electricity and adjusted net national income per capita across different countries, while considering the impact of population and country size. The analysis includes exploratory data visualizations and a linear regression model to uncover trends and relationships.
Data Preparation
# Load the dataset
data <- read.csv("data/merged_data_elect.csv")
# Check for missing values to assess data completeness
summary(data)## Country_Name Country_Code Year Access_to_Electricity
## Length:550 Length:550 Min. :2000 Min. : 3.10
## Class :character Class :character 1st Qu.:2005 1st Qu.: 60.65
## Mode :character Mode :character Median :2010 Median : 96.15
## Mean :2010 Mean : 78.22
## 3rd Qu.:2016 3rd Qu.:100.00
## Max. :2021 Max. :100.00
##
## Adjusted_Net_National_Income_Per_Capita Population Population_density
## Min. : 141.2 Min. :8.910e+04 Min. : 5.504
## 1st Qu.: 788.6 1st Qu.:1.145e+07 1st Qu.: 17.836
## Median : 2306.4 Median :3.277e+07 Median : 74.391
## Mean :10414.1 Mean :1.676e+08 Mean : 164.680
## 3rd Qu.:14330.6 3rd Qu.:1.467e+08 3rd Qu.: 217.487
## Max. :64743.9 Max. :1.412e+09 Max. :1301.039
## NA's :12
## Surface_area CO2_Emissions
## Min. : 180 Min. : 0.246
## 1st Qu.: 243610 1st Qu.: 30.529
## Median : 796100 Median : 118.870
## Mean : 2461119 Mean : 242.174
## 3rd Qu.: 1285220 3rd Qu.: 361.936
## Max. :17098250 Max. :4143.910
## NA's :230Exploratory Data Analysis
Trends in Access to Electricity by Continent
This plot shows how access to electricity has evolved over the years, grouped by continent. This helps identify regional trends and variations.
# Add continent information (assuming data contains this or can be joined)
# Example code assumes `Continent` is available in the data or a lookup is used
world <- ne_countries(scale = "medium", returnclass = "sf")
data <- data %>%
left_join(world %>% select(admin, continent), by = c("Country_Name" = "admin"))
# Plot trends grouped by continent
ggplot(data, aes(x = Year, y = Access_to_Electricity, color = continent)) +
geom_line(stat = "summary", fun = "mean") +
labs(title = "Trends in Access to Electricity by Continent", x = "Year", y = "Access to Electricity (%)", color = "Continent") +
theme_minimal()
Scatter Plot: Electricity vs Income
This scatter plot illustrates the relationship between access to electricity and adjusted net national income per capita. Each point represents a country-year observation. A smooth line is added to show the general trend in the data.
ggplot(data, aes(x = Adjusted_Net_National_Income_Per_Capita, y = Access_to_Electricity)) +
geom_point(aes(color = Country_Name)) +
geom_smooth(method = "lm", se = FALSE, color = "black") +
labs(title = "Access to Electricity vs Income", x = "Income per Capita (USD)", y = "Access to Electricity (%)", color = "Country") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 12 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 12 rows containing missing values or values outside the scale range
## (`geom_point()`).
Population Density and Electricity Access
This bar chart provides insights into how population density correlates with access to electricity across countries. Countries are grouped into categories based on their population density.
data <- data %>%
mutate(Population_Density_Category = cut(Population_density, breaks = c(0, 50, 100, 200, 500, Inf),
labels = c("0-50", "51-100", "101-200", "201-500", ">500")))
ggplot(data, aes(x = Population_Density_Category, y = Access_to_Electricity)) +
geom_boxplot() +
labs(title = "Access to Electricity by Population Density", x = "Population Density (people per sq. km)", y = "Access to Electricity (%)") +
theme_minimal()
Geographical Maps
This map visualizes access to electricity across different countries. Countries are shaded according to their electricity access percentage. Additionally, an interactive map for income per capita is included for enhanced exploration.
Access to Electricity
world <- ne_countries(scale = "medium", returnclass = "sf")
world_data <- world %>%
left_join(data, by = c("iso_a3" = "Country_Code"))
leaflet(world_data) %>%
addTiles() %>%
addPolygons(fillColor = ~colorNumeric("YlGn", Access_to_Electricity)(Access_to_Electricity),
color = "black", weight = 1, opacity = 1, fillOpacity = 0.7,
label = ~paste(admin, "Electricity:", Access_to_Electricity, "%")) %>%
addLegend(pal = colorNumeric("YlGn", world_data$Access_to_Electricity), values = world_data$Access_to_Electricity,
title = "Access to Electricity (%)")